{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Otto商品分类——LightGBM\n",
    "tfidf特征"
   ]
  },
  {
   "attachments": {
    "ff.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. 为什么Bagging能改进模型性能？（10分）\n",
    "\n",
    "![ff.png](attachment:ff.png)\n",
    "\n",
    "Bagging的优势在于当原始样本中有噪声数据时，通过bagging抽样，那么就有1/3的噪声样本不会被训练。对于受噪声影响的分类器，bagging对模型是有帮助的。所以说bagging可以降低模型的方差，不容易受噪声的影响，广泛应用在不稳定的模型，或者倾向于过拟合的模型。\n",
    "\n",
    " 2. 随机森林中随机体现在哪些方面？（10分） \n",
    "    随机森林是采用自助法（bootstrap）重采样技术，从原始训练集样本集N有放回的重复抽取k个样本形成新的训练集样本集合，然后根据自助样本集生成k个决策树组成的随机森林。新数据的分类结果按决策树投票多少形成的分数的决定。\n",
    "    特征选择采用随机的方法去分裂每个节点，然后比较不同情况下产生的误差，能够监测到内在估计误差、分类能力和相关性来选择特征的数目。单科决策树的分类能力很小，但在随机产生大量的决策树后，一个测试样本可以通过每一棵树的分类结果经统计后选择最可能的分类。\n",
    "\n",
    "\n",
    "3. 随机森林和GBDT的基学习器都是决策树。请问这两种模型中的决策树有什么不同。（10分） \n",
    "（1）传统GBDT以CART作为基分类器，xgboost还支持线性分类器，这个时候xgboost相当于带L1和L2正则化项的逻辑斯蒂回归（分类问题）或者线性回归（回归问题）。\n",
    "（2）传统GBDT在优化时只用到一阶导数信息，xgboost则对代价函数进行了二阶泰勒展开，同时用到了一阶和二阶导数。顺便提一下，xgboost工具支持自定义代价函数，只要函数可一阶和二阶求导。 \n",
    "（3）xgboost在代价函数里加入了正则项，用于控制模型的复杂度。正则项里包含了树的叶子节点个数、每个叶子节点上输出的score的L2模的平方和。从Bias-variance tradeoff角度来讲，正则项降低了模型的variance，使学习出来的模型更加简单，防止过拟合，这也是xgboost优于传统GBDT的一个特性。 \n",
    "（4）Shrinkage（缩减），相当于学习速率（xgboost中的eta）。xgboost在进行完一次迭代后，会将叶子节点的权重乘上该系数，主要是为了削弱每棵树的影响，让后面有更大的学习空间。实际应用中，一般把eta设置得小一点，然后迭代次数设置得大一点。（补充：传统GBDT的实现也有学习速率） \n",
    "（5）列抽样（column subsampling）。xgboost借鉴了随机森林的做法，支持列抽样，不仅能降低过拟合，还能减少计算，这也是xgboost异于传统gbdt的一个特性。\n",
    "（6）对缺失值的处理。对于特征的值有缺失的样本，xgboost可以自动学习出它的分裂方向。 \n",
    "（7）xgboost工具支持并行。boosting不是一种串行的结构吗?怎么并行的？注意xgboost的并行不是tree粒度的并行，xgboost也是一次迭代完才能进行下一次迭代的（第t次迭代的代价函数里包含了前面t-1次迭代的预测值）。xgboost的并行是在特征粒度上的。我们知道，决策树的学习最耗时的一个步骤就是对特征的值进行排序（因为要确定最佳分割点），xgboost在训练之前，预先对数据进行了排序，然后保存为block结构，后面的迭代中重复地使用这个结构，大大减小计算量。这个block结构也使得并行成为了可能，在进行节点的分裂时，需要计算每个特征的增益，最终选增益最大的那个特征去做分裂，那么各个特征的增益计算就可以开多线程进行。\n",
    "（8）可并行的近似直方图算法。树节点在进行分裂时，我们需要计算每个特征的每个分割点对应的增益，即用贪心法枚举所有可能的分割点。当数据无法一次载入内存或者在分布式情况下，贪心算法效率就会变得很低，所以xgboost还提出了一种可并行的近似直方图算法，用于高效地生成候选的分割点。\n",
    "\n",
    "\n",
    "\n",
    "4. 请简述LightGBM的训练速度为什么比XGBoost快。（30分） \n",
    "1）xgboost采用的是level-wise的分裂策略，而lightGBM采用了leaf-wise的策略，区别是xgboost对每一层所有节点做无差别分裂，可能有些节点的增益非常小，对结果影响不大，但是xgboost也进行了分裂，带来了务必要的开销。 leaft-wise的做法是在当前所有叶子节点中选择分裂收益最大的节点进行分裂，如此递归进行，很明显leaf-wise这种做法容易过拟合，因为容易陷入比较高的深度中，因此需要对最大深度做限制，从而避免过拟合。\n",
    "（2）lightgbm使用了基于histogram的决策树算法，这一点不同与xgboost中的 exact 算法，histogram算法在内存和计算代价上都有不小优势。 \n",
    "-. 内存上优势：很明显，直方图算法的内存消耗为(#data* #features * 1Bytes)(因为对特征分桶后只需保存特征离散化之后的值)，而xgboost的exact算法内存消耗为：(2 * #data * #features* 4Bytes)，因为xgboost既要保存原始feature的值，也要保存这个值的顺序索引，这些值需要32位的浮点数来保存。 \n",
    "-. 计算上的优势，预排序算法在选择好分裂特征计算分裂收益时需要遍历所有样本的特征值，时间为(#data),而直方图算法只需要遍历桶就行了，时间为(#bin)\n",
    "（3）直方图做差加速 \n",
    "-. 一个子节点的直方图可以通过父节点的直方图减去兄弟节点的直方图得到，从而加速计算。\n",
    "（4）lightgbm支持直接输入categorical 的feature \n",
    "-. 在对离散特征分裂时，每个取值都当作一个桶，分裂时的增益算的是”是否属于某个category“的gain。类似于one-hot编码。\n",
    "（5）但实际上xgboost的近似直方图算法也类似于lightgbm这里的直方图算法，为什么xgboost的近似算法比lightgbm还是慢很多呢？ \n",
    "-. xgboost在每一层都动态构建直方图， 因为xgboost的直方图算法不是针对某个特定的feature，而是所有feature共享一个直方图(每个样本的权重是二阶导),所以每一层都要重新构建直方图，而lightgbm中对每个特征都有一个直方图，所以构建一次直方图就够了。 \n",
    "\n",
    "（6）lightgbm哪些方面做了并行？ \n",
    "-. feature parallel \n",
    "一般的feature parallel就是对数据做垂直分割（partiion data vertically，就是对属性分割），然后将分割后的数据分散到各个workder上，各个workers计算其拥有的数据的best splits point, 之后再汇总得到全局最优分割点。\n",
    "-. data parallel \n",
    "传统的data parallel是将对数据集进行划分，也叫 平行分割(partion data horizontally)， 分散到各个workers上之后，workers对得到的数据做直方图，汇总各个workers的直方图得到全局的直方图。 lightgbm也claim这个操作的通讯开销较大，lightgbm的做法是使用”Reduce Scatter“机制，不汇总所有直方图，只汇总不同worker的不同feature的直方图，在这个汇总的直方图上做split，最后同步。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 首先 import 必要的模块\n",
    "import pandas as pd \n",
    "import numpy as np\n",
    "\n",
    "import lightgbm as lgbm\n",
    "from lightgbm.sklearn import LGBMClassifier\n",
    "\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 读取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>feat_1_tfidf</th>\n",
       "      <th>feat_2_tfidf</th>\n",
       "      <th>feat_3_tfidf</th>\n",
       "      <th>feat_4_tfidf</th>\n",
       "      <th>feat_5_tfidf</th>\n",
       "      <th>feat_6_tfidf</th>\n",
       "      <th>feat_7_tfidf</th>\n",
       "      <th>feat_8_tfidf</th>\n",
       "      <th>feat_9_tfidf</th>\n",
       "      <th>...</th>\n",
       "      <th>feat_85_tfidf</th>\n",
       "      <th>feat_86_tfidf</th>\n",
       "      <th>feat_87_tfidf</th>\n",
       "      <th>feat_88_tfidf</th>\n",
       "      <th>feat_89_tfidf</th>\n",
       "      <th>feat_90_tfidf</th>\n",
       "      <th>feat_91_tfidf</th>\n",
       "      <th>feat_92_tfidf</th>\n",
       "      <th>feat_93_tfidf</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.081393</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.075886</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Class_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.231403</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Class_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.199730</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Class_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.011987</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.011668</td>\n",
       "      <td>0.105971</td>\n",
       "      <td>0.021681</td>\n",
       "      <td>0.080435</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.008244</td>\n",
       "      <td>0.022456</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Class_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.124622</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.145988</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Class_1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 95 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  feat_1_tfidf  feat_2_tfidf  feat_3_tfidf  feat_4_tfidf  feat_5_tfidf  \\\n",
       "0   1      0.081393           0.0           0.0      0.000000      0.000000   \n",
       "1   2      0.000000           0.0           0.0      0.000000      0.000000   \n",
       "2   3      0.000000           0.0           0.0      0.000000      0.000000   \n",
       "3   4      0.011987           0.0           0.0      0.011668      0.105971   \n",
       "4   5      0.000000           0.0           0.0      0.000000      0.000000   \n",
       "\n",
       "   feat_6_tfidf  feat_7_tfidf  feat_8_tfidf  feat_9_tfidf  ...  feat_85_tfidf  \\\n",
       "0      0.000000      0.000000      0.000000           0.0  ...       0.075886   \n",
       "1      0.000000      0.000000      0.231403           0.0  ...       0.000000   \n",
       "2      0.000000      0.000000      0.199730           0.0  ...       0.000000   \n",
       "3      0.021681      0.080435      0.000000           0.0  ...       0.000000   \n",
       "4      0.000000      0.000000      0.000000           0.0  ...       0.124622   \n",
       "\n",
       "   feat_86_tfidf  feat_87_tfidf  feat_88_tfidf  feat_89_tfidf  feat_90_tfidf  \\\n",
       "0       0.000000       0.000000            0.0            0.0       0.000000   \n",
       "1       0.000000       0.000000            0.0            0.0       0.000000   \n",
       "2       0.000000       0.000000            0.0            0.0       0.000000   \n",
       "3       0.008244       0.022456            0.0            0.0       0.000000   \n",
       "4       0.000000       0.000000            0.0            0.0       0.145988   \n",
       "\n",
       "   feat_91_tfidf  feat_92_tfidf  feat_93_tfidf   target  \n",
       "0            0.0            0.0            0.0  Class_1  \n",
       "1            0.0            0.0            0.0  Class_1  \n",
       "2            0.0            0.0            0.0  Class_1  \n",
       "3            0.0            0.0            0.0  Class_1  \n",
       "4            0.0            0.0            0.0  Class_1  \n",
       "\n",
       "[5 rows x 95 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 读取数据\n",
    "# 这里使用原始特征+tf_idf特征，log(x+1)特征为原始特的单调变换，加上log特征对决策树模型影响不大\n",
    "# path to where the data lies\n",
    "dpath = './data/'\n",
    "\n",
    "train = pd.read_csv(dpath +\"Otto_FE_train_tfidf.csv\")\n",
    "\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 准备数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 将类别字符串变成数字，LightGBM不支持字符串格式的特征输入/标签输入\n",
    "y_train = train['target'] #形式为Class_x\n",
    "y_train = y_train.map(lambda s: s[6:])\n",
    "y_train = y_train.map(lambda s: int(s) - 1)#将类别的形式由Class_x变为0-8之间的整数\n",
    "\n",
    "X_train = train.drop([\"id\", \"target\"], axis=1)\n",
    "\n",
    "#保存特征名字以备后用（可视化）\n",
    "feat_names = X_train.columns \n",
    "\n",
    "#sklearn的学习器大多之一稀疏数据输入，模型训练会快很多\n",
    "#查看一个学习器是否支持稀疏数据，可以看fit函数是否支持: X: {array-like, sparse matrix}.\n",
    "#可自行用timeit比较稠密数据和稀疏数据的训练时间\n",
    "from scipy.sparse import csr_matrix\n",
    "X_train = csr_matrix(X_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LightGBM超参数调优"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "LightGBM的主要的超参包括：\n",
    "1. 树的数目n_estimators 和 学习率 learning_rate\n",
    "2. 树的最大深度max_depth 和 树的最大叶子节点数目num_leaves（注意：XGBoost只有max_depth，LightGBM采用叶子优先的方式生成树，num_leaves很重要，设置成比 2^max_depth 小）\n",
    "3. 叶子结点的最小样本数:min_data_in_leaf(min_data, min_child_samples)\n",
    "4. 每棵树的列采样比例：feature_fraction/colsample_bytree\n",
    "5. 每棵树的行采样比例：bagging_fraction （需同时设置bagging_freq=1）/subsample\n",
    "6. 正则化参数lambda_l1(reg_alpha), lambda_l2(reg_lambda)\n",
    "\n",
    "7. 两个非模型复杂度参数，但会影响模型速度和精度。可根据特征取值范围和样本数目修改这两个参数\n",
    "1）特征的最大bin数目max_bin：默认255；\n",
    "2）用来建立直方图的样本数目subsample_for_bin：默认200000。\n",
    "\n",
    "对n_estimators，用LightGBM内嵌的cv函数调优，因为同XGBoost一样，LightGBM学习的过程内嵌了cv，速度极快。\n",
    "其他参数用GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "MAX_ROUNDS = 10000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 相同的交叉验证分组"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# prepare cross validation\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "\n",
    "kfold = StratifiedKFold(n_splits=3, shuffle=True, random_state=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. n_estimators"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#直接调用lightgbm内嵌的交叉验证(cv)，可对连续的n_estimators参数进行快速交叉验证\n",
    "#而GridSearchCV只能对有限个参数进行交叉验证，且速度相对较慢\n",
    "def get_n_estimators(params , X_train , y_train , early_stopping_rounds=10):\n",
    "    lgbm_params = params.copy()\n",
    "    lgbm_params['num_class'] = 9\n",
    "     \n",
    "    lgbmtrain = lgbm.Dataset(X_train , y_train )\n",
    "     \n",
    "    #num_boost_round为弱分类器数目，下面的代码参数里因为已经设置了early_stopping_rounds\n",
    "    #即性能未提升的次数超过过早停止设置的数值，则停止训练\n",
    "    cv_result = lgbm.cv(lgbm_params , lgbmtrain , num_boost_round=MAX_ROUNDS , nfold=3,  metrics='multi_logloss' , early_stopping_rounds=early_stopping_rounds,seed=3 )\n",
    "     \n",
    "    print('best n_estimators:' , len(cv_result['multi_logloss-mean']))\n",
    "    print('best cv score:' , cv_result['multi_logloss-mean'][-1])\n",
    "     \n",
    "    return len(cv_result['multi_logloss-mean'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best n_estimators: 419\n",
      "best cv score: 0.48725748970351596\n"
     ]
    }
   ],
   "source": [
    "params = {'boosting_type': 'gbdt',\n",
    "          'objective': 'multiclass',\n",
    "          'n_jobs': 4,\n",
    "          'learning_rate': 0.1,\n",
    "          'num_leaves': 60,\n",
    "          'max_depth': 6,\n",
    "          'max_bin': 127, #2^6,原始特征为整数，很少超过100\n",
    "          'subsample': 0.7,\n",
    "          'bagging_freq': 1,\n",
    "          'colsample_bytree': 0.7,\n",
    "         }\n",
    "\n",
    "n_estimators_1 = get_n_estimators(params , X_train , y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. num_leaves & max_depth=6\n",
    "num_leaves建议70-80，搜索区间50-80."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 4 candidates, totalling 12 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=4)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done   8 out of  12 | elapsed:  3.2min remaining:  1.6min\n",
      "[Parallel(n_jobs=4)]: Done  12 out of  12 | elapsed:  4.2min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=StratifiedKFold(n_splits=3, random_state=3, shuffle=True),\n",
       "       error_score='raise-deprecating',\n",
       "       estimator=LGBMClassifier(bagging_freq=1, boosting_type='gbdt', class_weight=None,\n",
       "        colsample_bytree=0.7, importance_type='split', learning_rate=0.1,\n",
       "        max_bin=127, max_depth=6, min_child_samples=20,\n",
       "        min_child_weight=0.001, min_split_gain=0.0, n_estimators=419,\n",
       "        n_jobs=4, num_class=9, num_leaves=31, objective='multiclass',\n",
       "        random_state=None, reg_alpha=0.0, reg_lambda=0.0, silent=False,\n",
       "        subsample=0.7, subsample_for_bin=200000, subsample_freq=0),\n",
       "       fit_params=None, iid='warn', n_jobs=4,\n",
       "       param_grid={'num_leaves': range(50, 90, 10)},\n",
       "       pre_dispatch='2*n_jobs', refit=False, return_train_score='warn',\n",
       "       scoring='neg_log_loss', verbose=5)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params = {'boosting_type': 'gbdt',\n",
    "          'objective': 'multiclass',\n",
    "          'num_class':9, \n",
    "          'n_jobs': 4,\n",
    "          'learning_rate': 0.1,\n",
    "          'n_estimators':n_estimators_1,\n",
    "          'max_depth': 6,\n",
    "          'max_bin': 127, #2^6,原始特征为整数，很少超过100\n",
    "          'subsample': 0.7,\n",
    "          'bagging_freq': 1,\n",
    "          'colsample_bytree': 0.7,\n",
    "         }\n",
    "lg = LGBMClassifier(silent=False,  **params)\n",
    "\n",
    "num_leaves_s = range(50,90,10) #50,60,70,80\n",
    "tuned_parameters = dict( num_leaves = num_leaves_s)\n",
    "\n",
    "grid_search = GridSearchCV(lg, n_jobs=4, param_grid=tuned_parameters, cv = kfold, scoring=\"neg_log_loss\", verbose=5, refit = False)\n",
    "grid_search.fit(X_train , y_train)\n",
    "#grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.48649922455880884\n",
      "{'num_leaves': 70}\n"
     ]
    }
   ],
   "source": [
    "# examine the best model\n",
    "print(-grid_search.best_score_)\n",
    "print(grid_search.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot CV误差曲线\n",
    "test_means = grid_search.cv_results_[ 'mean_test_score' ]\n",
    "test_stds = grid_search.cv_results_[ 'std_test_score' ]\n",
    "train_means = grid_search.cv_results_[ 'mean_train_score' ]\n",
    "train_stds = grid_search.cv_results_[ 'std_train_score' ]\n",
    "\n",
    "n_leafs = len(num_leaves_s)\n",
    "\n",
    "x_axis = num_leaves_s\n",
    "plt.plot(x_axis, -test_means)\n",
    "#plt.errorbar(x_axis, -test_means, yerr=test_stds,label = ' Test')\n",
    "#plt.errorbar(x_axis, -train_means, yerr=train_stds,label = ' Train')\n",
    "plt.xlabel( 'num_leaves' )\n",
    "plt.ylabel( 'Log Loss' )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.48763183, -0.48714806, -0.48649922, -0.48649922])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_means"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 为减小魔影复杂度，防止过拟合，取系统推荐值：70, 不必再细调"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. min_child_samples\n",
    "叶子节点的最小样本数目\n",
    "\n",
    "叶子节点数目：70，共9类，平均每类8个叶子节点\n",
    "每棵树的样本数目数目最少的类（稀有事件）的样本数目：200 * 2/3 * 0.7 = 100\n",
    "所以每个叶子节点约100/8 = 12个样本点\n",
    "\n",
    "搜索范围：20-50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 3 candidates, totalling 9 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=4)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done   4 out of   9 | elapsed:  2.0min remaining:  2.5min\n",
      "[Parallel(n_jobs=4)]: Done   6 out of   9 | elapsed:  2.4min remaining:  1.2min\n",
      "[Parallel(n_jobs=4)]: Done   9 out of   9 | elapsed:  3.2min finished\n"
     ]
    },
    {
     "ename": "AttributeError",
     "evalue": "'GridSearchCV' object has no attribute 'best_estimator_'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-17-0fae757800ed>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     19\u001b[0m \u001b[0mgrid_search\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mGridSearchCV\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlg\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m,\u001b[0m  \u001b[0mparam_grid\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtuned_parameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcv\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkfold\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mscoring\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"neg_log_loss\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrefit\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     20\u001b[0m \u001b[0mgrid_search\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m \u001b[1;33m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 21\u001b[1;33m \u001b[0mgrid_search\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbest_estimator_\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m: 'GridSearchCV' object has no attribute 'best_estimator_'"
     ]
    }
   ],
   "source": [
    "params = {'boosting_type': 'gbdt',\n",
    "          'objective': 'multiclass',\n",
    "          'num_class':9, \n",
    "          'n_jobs': 4,\n",
    "          'learning_rate': 0.1,\n",
    "          'n_estimators':n_estimators_1,\n",
    "          'max_depth': 6,\n",
    "          'num_leaves':70,\n",
    "          'max_bin': 127, #2^6,原始特征为整数，很少超过100\n",
    "          'subsample': 0.7,\n",
    "          'bagging_freq': 1,\n",
    "          'colsample_bytree': 0.7,\n",
    "         }\n",
    "lg = LGBMClassifier(silent=False,  **params)\n",
    "\n",
    "min_child_samples_s = range(20,50,10) \n",
    "tuned_parameters = dict( min_child_samples = min_child_samples_s)\n",
    "\n",
    "grid_search = GridSearchCV(lg, n_jobs=4,  param_grid=tuned_parameters, cv = kfold, scoring=\"neg_log_loss\", verbose=5, refit = False)\n",
    "grid_search.fit(X_train , y_train)\n",
    "grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.48649922455880884\n",
      "{'min_child_samples': 20}\n"
     ]
    }
   ],
   "source": [
    "# examine the best model\n",
    "print(-grid_search.best_score_)\n",
    "print(grid_search.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:125: FutureWarning: You are accessing a training score ('mean_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "D:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:125: FutureWarning: You are accessing a training score ('std_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot CV误差曲线\n",
    "test_means = grid_search.cv_results_[ 'mean_test_score' ]\n",
    "test_stds = grid_search.cv_results_[ 'std_test_score' ]\n",
    "train_means = grid_search.cv_results_[ 'mean_train_score' ]\n",
    "train_stds = grid_search.cv_results_[ 'std_train_score' ]\n",
    "\n",
    "x_axis = min_child_samples_s\n",
    "\n",
    "plt.plot(x_axis, -test_means)\n",
    "#plt.errorbar(x_axis, -test_scores, yerr=test_stds ,label = ' Test')\n",
    "#plt.errorbar(x_axis, -train_scores, yerr=train_stds,label =  +' Train')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.48649922, -0.48685013, -0.48758023])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_means"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### min_child_samples=20"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 行采样参数 sub_samples/bagging_fraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 4 candidates, totalling 12 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=4)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done   8 out of  12 | elapsed:  4.1min remaining:  2.0min\n",
      "[Parallel(n_jobs=4)]: Done  12 out of  12 | elapsed:  5.3min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=StratifiedKFold(n_splits=3, random_state=3, shuffle=True),\n",
       "       error_score='raise-deprecating',\n",
       "       estimator=LGBMClassifier(bagging_freq=1, boosting_type='gbdt', class_weight=None,\n",
       "        colsample_bytree=0.7, importance_type='split', learning_rate=0.1,\n",
       "        max_bin=127, max_depth=6, min_child_samples=20,\n",
       "        min_child_weight=0.001, min_split_gain=0.0, n_estimators=419,\n",
       "        n_jobs=4, num_class=9, num_leaves=70, objective='multiclass',\n",
       "        random_state=None, reg_alpha=0.0, reg_lambda=0.0, silent=False,\n",
       "        subsample=1.0, subsample_for_bin=200000, subsample_freq=0),\n",
       "       fit_params=None, iid='warn', n_jobs=4,\n",
       "       param_grid={'subsample': [0.6, 0.7, 0.8, 0.9]},\n",
       "       pre_dispatch='2*n_jobs', refit=False, return_train_score='warn',\n",
       "       scoring='neg_log_loss', verbose=5)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params = {'boosting_type': 'gbdt',\n",
    "          'objective': 'multiclass',\n",
    "          'num_class':9, \n",
    "          'n_jobs': 4,\n",
    "          'learning_rate': 0.1,\n",
    "          'n_estimators':n_estimators_1,\n",
    "          'max_depth': 6,\n",
    "          'num_leaves':70,\n",
    "          'min_child_samples':20,\n",
    "          'max_bin': 127, #2^6,原始特征为整数，很少超过100\n",
    "          #'subsample': 0.7,\n",
    "          'bagging_freq': 1,\n",
    "          'colsample_bytree': 0.7,\n",
    "         }\n",
    "lg = LGBMClassifier(silent=False,  **params)\n",
    "\n",
    "subsample_s = [i/10.0 for i in range(6,10)]\n",
    "tuned_parameters = dict( subsample = subsample_s)\n",
    "\n",
    "grid_search = GridSearchCV(lg, n_jobs=4,  param_grid=tuned_parameters, cv = kfold, scoring=\"neg_log_loss\", verbose=5, refit = False)\n",
    "grid_search.fit(X_train , y_train)\n",
    "#grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.48649922455880884\n",
      "{'subsample': 0.7}\n"
     ]
    }
   ],
   "source": [
    "# examine the best model\n",
    "print(-grid_search.best_score_)\n",
    "print(grid_search.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:125: FutureWarning: You are accessing a training score ('mean_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "D:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:125: FutureWarning: You are accessing a training score ('std_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot CV误差曲线\n",
    "test_means = grid_search.cv_results_[ 'mean_test_score' ]\n",
    "test_stds = grid_search.cv_results_[ 'std_test_score' ]\n",
    "train_means = grid_search.cv_results_[ 'mean_train_score' ]\n",
    "train_stds = grid_search.cv_results_[ 'std_train_score' ]\n",
    "\n",
    "x_axis = subsample_s\n",
    "\n",
    "plt.plot(x_axis, -test_means)\n",
    "#plt.errorbar(x_axis, -test_scores[:,i], yerr=test_stds[:,i] ,label = str(max_depths[i]) +' Test')\n",
    "#plt.errorbar(x_axis, -train_scores[:,i], yerr=train_stds[:,i] ,label = str(max_depths[i]) +' Train')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.48907679, -0.48649922, -0.48737878, -0.48843849])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_means"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### subsample=0.7"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 列采样参数 sub_feature/feature_fraction/colsample_bytree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 5 candidates, totalling 15 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=4)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  12 out of  15 | elapsed:  4.7min remaining:  1.2min\n",
      "[Parallel(n_jobs=4)]: Done  15 out of  15 | elapsed:  5.1min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=StratifiedKFold(n_splits=3, random_state=3, shuffle=True),\n",
       "       error_score='raise-deprecating',\n",
       "       estimator=LGBMClassifier(bagging_freq=1, boosting_type='gbdt', class_weight=None,\n",
       "        colsample_bytree=1.0, importance_type='split', learning_rate=0.1,\n",
       "        max_bin=127, max_depth=6, min_child_samples=20,\n",
       "        min_child_weight=0.001, min_split_gain=0.0, n_estimators=419,\n",
       "        n_jobs=4, num_class=9, num_leaves=70, objective='multiclass',\n",
       "        random_state=None, reg_alpha=0.0, reg_lambda=0.0, silent=False,\n",
       "        subsample=0.7, subsample_for_bin=200000, subsample_freq=0),\n",
       "       fit_params=None, iid='warn', n_jobs=4,\n",
       "       param_grid={'colsample_bytree': [0.2, 0.3, 0.4, 0.5, 0.6]},\n",
       "       pre_dispatch='2*n_jobs', refit=False, return_train_score='warn',\n",
       "       scoring='neg_log_loss', verbose=5)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params = {'boosting_type': 'gbdt',\n",
    "          'objective': 'multiclass',\n",
    "          'num_class':9, \n",
    "          'n_jobs': 4,\n",
    "          'learning_rate': 0.1,\n",
    "          'n_estimators':n_estimators_1,\n",
    "          'max_depth': 6,\n",
    "          'num_leaves':70,\n",
    "          'min_child_samples':20,\n",
    "          'max_bin': 127, #2^6,原始特征为整数，很少超过100\n",
    "          'subsample': 0.7,\n",
    "          'bagging_freq': 1,\n",
    "          #'colsample_bytree': 0.7,\n",
    "         }\n",
    "lg = LGBMClassifier(silent=False,  **params)\n",
    "\n",
    "colsample_bytree_s = [i/10.0 for i in range(2,7)]\n",
    "tuned_parameters = dict( colsample_bytree = colsample_bytree_s)\n",
    "\n",
    "grid_search = GridSearchCV(lg, n_jobs=4,  param_grid=tuned_parameters, cv = kfold, scoring=\"neg_log_loss\", verbose=5, refit = False)\n",
    "grid_search.fit(X_train , y_train)\n",
    "#grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.4876128933343309\n",
      "{'colsample_bytree': 0.6}\n"
     ]
    }
   ],
   "source": [
    "# examine the best model\n",
    "print(-grid_search.best_score_)\n",
    "print(grid_search.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:125: FutureWarning: You are accessing a training score ('mean_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "D:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:125: FutureWarning: You are accessing a training score ('std_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot CV误差曲线\n",
    "test_means = grid_search.cv_results_[ 'mean_test_score' ]\n",
    "test_stds = grid_search.cv_results_[ 'std_test_score' ]\n",
    "train_means = grid_search.cv_results_[ 'mean_train_score' ]\n",
    "train_stds = grid_search.cv_results_[ 'std_train_score' ]\n",
    "\n",
    "x_axis = colsample_bytree_s\n",
    "\n",
    "plt.plot(x_axis, -test_means)\n",
    "#plt.errorbar(x_axis, -test_scores[:,i], yerr=test_stds[:,i] ,label = str(max_depths[i]) +' Test')\n",
    "#plt.errorbar(x_axis, -train_scores[:,i], yerr=train_stds[:,i] ,label = str(max_depths[i]) +' Train')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### colsample_bytree=0.6"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 正则化参数lambda_l1(reg_alpha), lambda_l2(reg_lambda)感觉不用调了"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 减小学习率，调整n_estimators"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best n_estimators: 4203\n",
      "best cv score: 0.478885635538274\n"
     ]
    }
   ],
   "source": [
    "params = {'boosting_type': 'gbdt',\n",
    "          'objective': 'multiclass',\n",
    "          'num_class':9, \n",
    "          'n_jobs': 5,\n",
    "          'learning_rate': 0.01,\n",
    "          #'n_estimators':n_estimators_1,\n",
    "          'max_depth': 6,\n",
    "          'num_leaves':70,\n",
    "          'min_child_samples':20,\n",
    "          'max_bin': 127, #2^6,原始特征为整数，很少超过100\n",
    "          'subsample': 0.7,\n",
    "          'bagging_freq': 1,\n",
    "          'colsample_bytree': 0.6,\n",
    "         }\n",
    "n_estimators_2 = get_n_estimators(params , X_train , y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 用所有训练数据，采用最佳参数重新训练模型\n",
    "由于样本数目增多，模型复杂度稍微扩大一点？\n",
    "num_leaves增多5\n",
    "min_child_samples按样本比例增加到40"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LGBMClassifier(bagging_freq=1, boosting_type='gbdt', class_weight=None,\n",
       "        colsample_bytree=0.6, importance_type='split', learning_rate=0.01,\n",
       "        max_bin=127, max_depth=6, min_child_samples=40,\n",
       "        min_child_weight=0.001, min_split_gain=0.0, n_estimators=4203,\n",
       "        n_jobs=5, num_class=9, num_leaves=75, objective='multiclass',\n",
       "        random_state=None, reg_alpha=0.0, reg_lambda=0.0, silent=False,\n",
       "        subsample=0.7, subsample_for_bin=200000, subsample_freq=0)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params = {'boosting_type': 'gbdt',\n",
    "          'objective': 'multiclass',\n",
    "          'num_class':9, \n",
    "          'n_jobs': 5,\n",
    "          'learning_rate': 0.01,\n",
    "          'n_estimators':n_estimators_2,\n",
    "          'max_depth': 6,\n",
    "          'num_leaves':75,\n",
    "          'min_child_samples':40,\n",
    "          'max_bin': 127, #2^6,原始特征为整数，很少超过100\n",
    "          'subsample': 0.7,\n",
    "          'bagging_freq': 1,\n",
    "          'colsample_bytree': 0.6,\n",
    "         }\n",
    "\n",
    "lg = LGBMClassifier(silent=False,  **params)\n",
    "lg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 保存模型，用于后续测试"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "pickle.dump(lg, open(\"Otto_LightGBM_homework_tfidf.pkl\", 'wb'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 特征重要性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame({\"columns\":list(feat_names), \"importance\":list(lg.feature_importances_.T)})\n",
    "df = df.sort_values(by=['importance'],ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>columns</th>\n",
       "      <th>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>feat_67_tfidf</td>\n",
       "      <td>41869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>feat_25_tfidf</td>\n",
       "      <td>40901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>feat_24_tfidf</td>\n",
       "      <td>38580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>feat_48_tfidf</td>\n",
       "      <td>38452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>feat_40_tfidf</td>\n",
       "      <td>35064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>feat_86_tfidf</td>\n",
       "      <td>33939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>feat_14_tfidf</td>\n",
       "      <td>31916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>feat_62_tfidf</td>\n",
       "      <td>26244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>feat_15_tfidf</td>\n",
       "      <td>23557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>feat_64_tfidf</td>\n",
       "      <td>23123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>feat_16_tfidf</td>\n",
       "      <td>23071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>feat_33_tfidf</td>\n",
       "      <td>21917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>feat_42_tfidf</td>\n",
       "      <td>21786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>feat_34_tfidf</td>\n",
       "      <td>21547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>feat_88_tfidf</td>\n",
       "      <td>20825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>feat_60_tfidf</td>\n",
       "      <td>19364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>feat_54_tfidf</td>\n",
       "      <td>19266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>feat_72_tfidf</td>\n",
       "      <td>18452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>feat_8_tfidf</td>\n",
       "      <td>18215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>feat_32_tfidf</td>\n",
       "      <td>17831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>feat_36_tfidf</td>\n",
       "      <td>16723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>feat_70_tfidf</td>\n",
       "      <td>16381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>feat_75_tfidf</td>\n",
       "      <td>16342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>feat_43_tfidf</td>\n",
       "      <td>16154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>feat_9_tfidf</td>\n",
       "      <td>15969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>feat_71_tfidf</td>\n",
       "      <td>14873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>feat_92_tfidf</td>\n",
       "      <td>14733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>feat_85_tfidf</td>\n",
       "      <td>13800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>feat_11_tfidf</td>\n",
       "      <td>13562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>feat_26_tfidf</td>\n",
       "      <td>13538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>feat_80_tfidf</td>\n",
       "      <td>7311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>feat_30_tfidf</td>\n",
       "      <td>7256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>feat_65_tfidf</td>\n",
       "      <td>7059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>feat_78_tfidf</td>\n",
       "      <td>7008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>feat_10_tfidf</td>\n",
       "      <td>6960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>feat_58_tfidf</td>\n",
       "      <td>6602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>feat_3_tfidf</td>\n",
       "      <td>6550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>feat_27_tfidf</td>\n",
       "      <td>5942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>feat_49_tfidf</td>\n",
       "      <td>5796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>feat_46_tfidf</td>\n",
       "      <td>5773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>feat_21_tfidf</td>\n",
       "      <td>5532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>feat_12_tfidf</td>\n",
       "      <td>5312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>feat_52_tfidf</td>\n",
       "      <td>4925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>feat_63_tfidf</td>\n",
       "      <td>4858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>feat_23_tfidf</td>\n",
       "      <td>4851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>feat_45_tfidf</td>\n",
       "      <td>4811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>feat_77_tfidf</td>\n",
       "      <td>4736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>feat_19_tfidf</td>\n",
       "      <td>4665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>feat_28_tfidf</td>\n",
       "      <td>4624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>feat_7_tfidf</td>\n",
       "      <td>4455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>feat_5_tfidf</td>\n",
       "      <td>4397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>feat_93_tfidf</td>\n",
       "      <td>4067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>feat_2_tfidf</td>\n",
       "      <td>3774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>feat_81_tfidf</td>\n",
       "      <td>3571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>feat_31_tfidf</td>\n",
       "      <td>3350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>feat_61_tfidf</td>\n",
       "      <td>2480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>feat_82_tfidf</td>\n",
       "      <td>2412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>feat_84_tfidf</td>\n",
       "      <td>2198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>feat_6_tfidf</td>\n",
       "      <td>1962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>feat_51_tfidf</td>\n",
       "      <td>1277</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>93 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          columns  importance\n",
       "66  feat_67_tfidf       41869\n",
       "24  feat_25_tfidf       40901\n",
       "23  feat_24_tfidf       38580\n",
       "47  feat_48_tfidf       38452\n",
       "39  feat_40_tfidf       35064\n",
       "85  feat_86_tfidf       33939\n",
       "13  feat_14_tfidf       31916\n",
       "61  feat_62_tfidf       26244\n",
       "14  feat_15_tfidf       23557\n",
       "63  feat_64_tfidf       23123\n",
       "15  feat_16_tfidf       23071\n",
       "32  feat_33_tfidf       21917\n",
       "41  feat_42_tfidf       21786\n",
       "33  feat_34_tfidf       21547\n",
       "87  feat_88_tfidf       20825\n",
       "59  feat_60_tfidf       19364\n",
       "53  feat_54_tfidf       19266\n",
       "71  feat_72_tfidf       18452\n",
       "7    feat_8_tfidf       18215\n",
       "31  feat_32_tfidf       17831\n",
       "35  feat_36_tfidf       16723\n",
       "69  feat_70_tfidf       16381\n",
       "74  feat_75_tfidf       16342\n",
       "42  feat_43_tfidf       16154\n",
       "8    feat_9_tfidf       15969\n",
       "70  feat_71_tfidf       14873\n",
       "91  feat_92_tfidf       14733\n",
       "84  feat_85_tfidf       13800\n",
       "10  feat_11_tfidf       13562\n",
       "25  feat_26_tfidf       13538\n",
       "..            ...         ...\n",
       "79  feat_80_tfidf        7311\n",
       "29  feat_30_tfidf        7256\n",
       "64  feat_65_tfidf        7059\n",
       "77  feat_78_tfidf        7008\n",
       "9   feat_10_tfidf        6960\n",
       "57  feat_58_tfidf        6602\n",
       "2    feat_3_tfidf        6550\n",
       "26  feat_27_tfidf        5942\n",
       "48  feat_49_tfidf        5796\n",
       "45  feat_46_tfidf        5773\n",
       "20  feat_21_tfidf        5532\n",
       "11  feat_12_tfidf        5312\n",
       "51  feat_52_tfidf        4925\n",
       "62  feat_63_tfidf        4858\n",
       "22  feat_23_tfidf        4851\n",
       "44  feat_45_tfidf        4811\n",
       "76  feat_77_tfidf        4736\n",
       "18  feat_19_tfidf        4665\n",
       "27  feat_28_tfidf        4624\n",
       "6    feat_7_tfidf        4455\n",
       "4    feat_5_tfidf        4397\n",
       "92  feat_93_tfidf        4067\n",
       "1    feat_2_tfidf        3774\n",
       "80  feat_81_tfidf        3571\n",
       "30  feat_31_tfidf        3350\n",
       "60  feat_61_tfidf        2480\n",
       "81  feat_82_tfidf        2412\n",
       "83  feat_84_tfidf        2198\n",
       "5    feat_6_tfidf        1962\n",
       "50  feat_51_tfidf        1277\n",
       "\n",
       "[93 rows x 2 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(range(len(lg.feature_importances_)), lg.feature_importances_)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
